Vectorial has built a proprietary behavior model — SAPIENS. Each population is learnt from the behavior of thousands of real patients and clinicians — drawn from public and enterprise sources, and from self-reported care experience acquired privately through licensed research panels.
Behavioral depth is the constraint in this category, not model architecture. SAPIENS learns each individual across the dimensions that actually move clinical behavior, and every one of them is derived from the behavior of real people.
Culture, community, belief systems, household composition and income tier.
Socio-demographic and psychographic traits — risk tolerance, trust, adherence, care-seeking style.
The settings and situations a patient moves through, and how their behavior changes across them.
The patient journey to date and prior exposure to treatments, clinicians and competing solutions.
A deep research agent finds the communities where a given patient or clinician population actually congregates, then ingests what they say unprompted — the closest thing to observing behavior without interrupting it.
We run our own structured interviews with real people in the condition area, through licensed panels, at a volume no research team can staff. These are ingested as first-party signal and used to enrich thin traits and correct the ones public sources got wrong.
Panel integration gives access to verified, consented respondents with known demographics — including healthcare-specialist panels of screened patients, caregivers, nurses and physicians. Used to fill gaps where a population is under-represented online, most often older, rural and lower-income patients.
The same patient behaves differently in a clinic room, on a med-surg floor and on a discharge call. Every modeled patient carries a journey stage, and the simulation runs inside it.
A knee-replacement patient describes pain very differently on post-op day two than on a week-two follow-up call. Pinning the simulation to the setting is what makes the transcript usable.
Live deployments at Midi Health and Everkind — actual audiences, with profile counts, signals and readiness as the platform reports them.
Treatment decisions aren't set by protocol alone. Every clinician brings their own philosophy and their own experience with patients — and how care feels is shaped as much by bedside behavior: how they build trust, how they question, how they extend empathy. We model that human side, not a robotic protocol. Populations are built from the communities where clinicians actually talk to each other, and every behavioral dimension is confidence-scored.
Specialty, care setting, years in practice, patient volume and case mix.
Evidence thresholds, risk tolerance, guideline adherence, and when judgment overrides protocol.
Who they believe and in what order — peer networks, journals, communities, institutions, industry reps.
Time pressure, documentation load, team structure, and how new tools actually get adopted or rejected.
Nurse and patient populations built for the settings Suki is deployed in — inpatient rooms and post-surgical wards. Below are the actual audiences, followed by full chat simulations generated from them.
Ambient models have to pull the clinical points out of a conversation between two people. Patients don't speak in clean medical terms — they meander and circle back. Clinicians each draw that information out differently. We model both sides, so you get a full encounter instead of a monologue, and human-like data fast instead of one real encounter at a time.
Run ambient against thousands of conversations where the patient rambles, hedges, contradicts themselves, or has a family member talk over them — and see where accuracy drops before a customer does.
Generate labeled training data across specialties, care settings and patient archetypes, with demographic ratios set to match your production distribution rather than whatever the last quarter happened to contain.
Behavioral coverage comes from modeling real people, so the edge cases are the ones that actually occur — low health literacy, language hedging, distrust, conflated conditions, disfluencies and realistic transcription error.
Suki’s products all sit inside the same encounter. Because both sides are modeled, that encounter can be simulated end to end — so quality gaps surface before production, not after.
Where we help: the conversation the note is built from — encounter dialogue at volume, fast and cheap, with the hedging real conversations carry.
Where we help: the clinical substance the code comes from — comorbidity profiles and treatment decisions spanning ICD-10, HCC, CPT and E/M, including the codes that are hard to reach.
Where we help: the case volume an insight has to be right across — partial histories, lay descriptions, and clinicians with their own evidence thresholds.
Transcripts generated from specific modeled profiles. Each profile’s background and journey stage set the scenario: the patient hedges and under-describes, the nurse probes and charts. Open any card for the full 24-turn transcript.
Ask a general-purpose model to play a 47-year-old with perimenopause and it gives you the reasonable answer — that's what it was trained to do. Real patients delay, distrust, self-treat and decide on cost and stigma. That behavior only survives if the population carries it in from real people.
The differences above are measurable. Accuracy is the % of opinions where the real opinion matches the generated one when a product is shown to a modeled user.
Benchmark developed with Berkeley AI Research (BAIR). Full methodology, per-model and per-domain results: SAPIENS Benchmarking Study.
When Vectorial showed the perimenopause audience simulations, it immediately made sense to us. The model captured behavioral traits and motivations consistent with what we see from real patients, which made the audience feel credible and specific.
Vectorial's value is that the personas are learnt from real people, not synthetic data — something no evaluation engine has been able to close the gap on for us in a meaningful way.
The chat simulations feel natural, representing real-life conversations between patients and nurses — and they are clinically accurate.
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